A groundbreaking AI-driven firefighting system from KIMM autonomously detects and precisely extinguishes oil fires aboard naval vessels, marking a global first in maritime fire safety and combat-readiness innovation.
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A next-generation fire suppression system, capable of autonomously detecting oil fires aboard naval vessels and precisely targeting and extinguishing them, even in maritime environments, has been developed domestically for the first time. The system utilizes AI to independently assess the authenticity of a fire, activating only when an actual fire is detected. It concentrates its discharge solely on the fire source, much like a firefighter extinguishing flames.
KIMM leads domestic development effort
A research team led by Senior Researcher Hyuk Lee at the AX Convergence Research Center, Virtual Engineering Platform Research Division, Korea Institute of Machinery and Materials (KIMM, President Seog-Hyeon Ryu), under the National Research Council of Science & Technology (NST, Chairman Young-Shik Kim), has developed an autonomous initial suppression firefighting system specialized for shipboard oil fires and completed real-ship trials on an actual vessel.
Autonomous system tailored for shipboard oil fires
This newly developed initial suppression firefighting system for shipboard oil fires represents an advanced iteration of the research team's autonomous firefighting technology, specifically designed for the most common oil fires that occur on naval vessels. Its key feature is the ability to autonomously detect oil fires caused by equipment or aircraft leaks in engine rooms, hangars, decks, and other areas, and accurately target and extinguish the fire source even under complex environmental conditions, such as sea waves and ship motion.
AI and reinforcement learning enable precision targeting
Existing shipboard firefighting systems release extinguishing agents throughout the entire affected area upon detection of a fire. This approach caused unnecessary damage during false alarms and made precise targeting difficult in maritime environments. In contrast, the technology developed by the KIMM research team combines AI-based precision fire detection with reinforcement learning algorithms for maritime condition adaptation, dramatically overcoming these limitations.
System architecture and performance verification
The developed fire suppression system comprises fire detection sensors, fire monitors, and an analysis and control unit equipped with AI-based capabilities for determining fire authenticity and estimating location. The system maintains a fire detection accuracy of over 98%, with a foam discharge range reaching approximately 24 meters. It has also been verified to operate stably even in sea states of 3 or higher.
Simulation-based validation of AI accuracy
The research team conducted systematic performance verification using a large-scale land-based simulation facility (25 m × 5 m × 5 m) that accurately replicates the actual ship environment. Within the simulation facility, which replicated the color and illumination of actual ship compartments, various oil fire conditions and non-fire situations that could be mistaken for fires (such as lighters, welding, and electric heaters) were reproduced to perform pre-training and accuracy testing of the AI system.
Proven effectiveness in realistic fire scenarios
Notably, successful suppression tests were completed for open-area oil fires (maximum 4.5㎡ m² oil tray) and shielded fires (a helicopter-sized shield installed 50cm above a 3.0㎡ m² oil tray) that could occur from leaks in aircraft carriers, proving the system's capability to respond to all types of oil fires possible on actual vessels.
Real-ship trials demonstrate maritime readiness
Subsequently, the research team conducted real-ship tests aboard the LST-II class amphibious assault ship (ROKS Ilchulbong). It successfully achieved precise targeting of extinguishing water onto a fire source 18m away in actual sea conditions with 1m waves. To accomplish this, they developed and pre-trained a reinforcement learning-based algorithm that recalculates the aiming angle in real-time by reflecting wave and hull motion using only 6-degree-of-freedom acceleration data.
Global first in autonomous maritime firefighting
Senior Researcher Hyuk Lee of KIMM stated, "This newly developed initial suppression firefighting system for shipboard oil fires is the world's first technology to complete step-by-step verification from land-based simulation facilities to actual shipboard environments." He added, "It can autonomously respond to the most dangerous oil fires on ships in both open and shielded conditions, marking a groundbreaking turning point for crew safety and preserving the ship's combat effectiveness." He further noted, "This technology is applicable not only to various naval vessels but also to ammunition depots, military supply warehouses, aircraft hangars, and offshore plants. Its future expansion to civilian ships and petrochemical facilities will significantly enhance fire safety at sea and in industrial settings."
Collaborative civilian-military research initiative
This research was conducted as part of the 'Civilian-Military Practical Application Linkage Project' implemented by the Institute of Civil Military Technology Cooperation. Participants included the Korea Institute of Civil Engineering and Building Technology, Chungnam National University, Super Century Co., Ltd., and the Korea Military Academy.
About the Korea Institute of Machinery and Materials
The Korea Institute of Machinery and Materials (KIMM) is a non-profit government-funded research institute under the Ministry of Science and ICT. Since its foundation in 1976, KIMM has contributed to the nation's economic growth by performing R&D on key technologies in machinery and materials, conducting reliability test evaluations, and commercializing the developed products and technologies.
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